In [3]:
import factors
import inspect
n = 0
regression=False; N = 1 # run particular factor/ regression
for name, obj in inspect.getmembers(factors, predicate=inspect.isclass):
    if obj.__module__ == "factors":
        if n==N or regression==True:
            # clear_output(wait=False)
            %run -i backtest.py {name}
        n = n+1

factor analysis begin:  Factor_DailyReturns 2024-02-23 02:07:25.321714
analysis factor/portfolio:  True/True
long-short/group neutral:  True/False
strategy:  eq_weight optimize:  max_sharpe
simulation_time:                 0
start 2014-01-01
end   2017-01-01
use_storage_data:  True
Dropped 18.1% entries from factor data: 18.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).
max_loss is 50.0%, not exceeded: OK!
        factors:  (755597, 2)
         prices:  (756, 1655)
     pf_returns:  (1086,)
   pf_positions:  (1086, 1218)
pf_transactions:  (8297, 8)
   pf_benchmark:  (1080,)
Quantiles Statistics
min max mean std count count %
factor_quantile
1 1.0 1570.0 387.059700 222.940334 124120 20.046936
2 341.0 2042.0 953.525436 244.402657 123682 19.976193
3 655.0 2421.0 1450.230185 259.104429 123683 19.976355
4 1071.0 2757.0 1952.623357 252.894963 123682 19.976193
5 1654.0 3137.0 2539.458316 254.748800 123980 20.024324 
Returns Analysis
1D 5D 10D
Ann. alpha 0.026 -0.003 -0.002
beta 0.024 0.039 0.056
Mean Period Wise Return Top Quantile (bps) 0.104 -0.383 -0.175
Mean Period Wise Return Bottom Quantile (bps) -2.264 -0.516 -0.364
Mean Period Wise Spread (bps) 2.368 0.112 0.158 
<Figure size 432x288 with 0 Axes>
Information Analysis
1D 5D 10D
IC Mean 0.003 -0.001 -0.001
IC Std. 0.135 0.137 0.134
Risk-Adjusted IC 0.020 -0.005 -0.005
t-stat(IC) 0.550 -0.132 -0.127
p-value(IC) 0.582 0.895 0.899
IC Skew 0.057 0.119 0.095
IC Kurtosis 0.595 0.689 0.789 
Turnover Analysis
10D 1D 5D
Quantile 1 Mean Turnover 0.776 0.767 0.776
Quantile 2 Mean Turnover 0.791 0.791 0.789
Quantile 3 Mean Turnover 0.779 0.765 0.776
Quantile 4 Mean Turnover 0.794 0.788 0.791
Quantile 5 Mean Turnover 0.767 0.761 0.762
1D 5D 10D
Mean Factor Rank Autocorrelation -0.014 0.003 0.006 
<Figure size 432x288 with 0 Axes>
Entire data start date: 2014-01-03
Entire data end date: 2016-12-22
Backtest months: 51
Backtest
Annual return 0.0%
Cumulative returns 0.0%
Annual volatility 1.1%
Sharpe ratio 0.01
Calmar ratio 0.00
Stability 0.06
Max drawdown -4.3%
Omega ratio 1.00
Sortino ratio 0.02
Skew -0.05
Kurtosis 4.72
Tail ratio 0.86
Daily value at risk -0.1%
Gross leverage 0.71
Daily turnover 4.2%
Alpha -0.00
Beta 0.01
Worst drawdown periods Net drawdown in % Peak date Valley date Recovery date Duration
0 4.31 2016-04-12 2016-11-15 NaT NaN
1 2.93 2014-07-08 2015-07-29 2016-03-24 448
2 0.62 2014-01-05 2014-01-23 2014-04-04 65
3 0.25 2014-04-08 2014-04-14 2014-04-17 8
4 0.22 2014-06-10 2014-06-20 2014-06-27 14
Stress Events mean min max
Apr14 0.04% -0.10% 0.19%
Oct14 -0.02% -0.24% 0.33%
Fall2015 0.01% -0.26% 0.24%
New Normal 0.00% -0.40% 0.33% 
In-sample months: 47
Out-of-sample months: 3
All In-sample Out-of-sample
Annual return -0.0% -0.1% 0.4%
Cumulative returns -0.1% -0.2% 0.1%
Annual volatility 1.1% 1.1% 1.2%
Sharpe ratio -0.02 -0.05 0.31
Calmar ratio -0.01 -0.02 0.22
Stability 0.05 0.00 0.02
Max drawdown -4.3% -3.0% -1.7%
Omega ratio 1.00 0.99 1.06
Sortino ratio -0.03 -0.06 0.45
Skew -0.05 -0.06 0.04
Kurtosis 4.70 5.10 1.06
Tail ratio 0.86 0.87 1.11
Daily value at risk -0.1% -0.1% -0.2%
Gross leverage 0.71 0.71 0.73
Daily turnover 4.2% 4.3% 3.3%
Alpha -0.00 -0.00 0.01
Beta 0.01 0.01 -0.01
In [3]:
from IPython.display import Javascript, clear_output
from nbconvert import HTMLExporter

def save_notebook():
    display(
        Javascript("IPython.notebook.save_notebook()"),
        include=['application/javascript']
    )

def output_HTML(current_file, output_file):
    import codecs
    import nbformat
    exporter = HTMLExporter()
    output_notebook = nbformat.read(current_file, as_version=4)
    output, resources = exporter.from_notebook_node(output_notebook)
    file = codecs.open(output_file, 'w', encoding='utf-8')
    file.write(output)
    file.close()

print('saved: ', name)
current_file = 'regression.ipynb'
output_file = f'results/factor_{name}.html'
output_HTML(current_file, output_file)

saved:  WeightedAverageValue